Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing

doi:10.1109/TGRS.2014.2365953

OPT OpenIR > 光谱成像技术研究室

	Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing
	Yuan, Yuan; Fu, Min; Lu, Xiaoqiang
	2015-06-01
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷号	53 期号:6 页码:2975-2986
摘要	Hyperspectral unmixing is one of the most important problems in analyzing remote sensing images, which aims to decompose a mixed pixel into a collection of constituent materials named endmembers and their corresponding fractional abundances. Recently, various methods have been proposed to incorporate sparse constraints into hyperspectral unmixing and achieve advanced performance. However, most of them ignore the complex distribution of substances in hyperspectral data so that they are only effective in limited cases. In this paper, the concept of substance dependence is introduced to help hyperspectral unmixing. Generally, substance dependence can be considered in a local region by K-nearest neighbors method. However, since substances of hyperspectral images are complicatedly distributed, number K of the most similar substances to each substance is difficult to decide. In this case, substance dependence should be considered in the whole data space, and the number of the K most similar substances to each substance can be adaptively determined by searching from the whole space. Through maintaining the substance dependence during unmixing, the abundances resulted from the proposed method are closer to the real fractions, which lead to better unmixing performance. The following contributions can be summarized. 1) The concept of substance dependence is proposed to describe the complicated relationship between substances in the hyperspectral image. 2) We propose substance dependence constrained sparse nonnegative matrix factorization ( SDSNMF) for hyperspectral unmixing. Using SDSNMF, we meet or exceed state-of-the-art unmixing performance. 3) Adequate experiments on both synthetic and real hyperspectral data have been tested. Compared with the state-of-the-art methods, the experimental results prove the superiority of the proposed method.
文章类型	Article
关键词	Adaptive Decision Hyperspectral Unmixing Mixed Pixel Substance Dependence
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/TGRS.2014.2365953
收录类别	SCI ; EI
关键词[WOS]	NONNEGATIVE MATRIX FACTORIZATION ; ENDMEMBER EXTRACTION ; COMPONENT ANALYSIS ; ALGORITHM ; IMAGERY ; REGULARIZATION ; SUBSPACE
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000351063800001
引用统计	被引频次：58[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/24119
专题	光谱成像技术研究室
作者单位	Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Yuan,Fu, Min,Lu, Xiaoqiang. Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2015,53(6):2975-2986.
APA	Yuan, Yuan,Fu, Min,&Lu, Xiaoqiang.(2015).Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,53(6),2975-2986.
MLA	Yuan, Yuan,et al."Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 53.6(2015):2975-2986.

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